scholarly journals A novel deep learning scheme for morphology-based classification of mycobacterial infection in unstained macrophages

2019 ◽  
Author(s):  
Xinzhuo Zhao ◽  
Yanqing Bao ◽  
Lin Wang ◽  
Wei Qian ◽  
Jianjun Sun
Keyword(s):  
Deep Learning ◽  
Bacterial Infections ◽  
Morphological Changes ◽  
Model Organism ◽  
Bacterial Pathogen ◽  
Mycobacterial Infection ◽  
Fine Tuning ◽  
Great Promise ◽  
Morphological Differences ◽  
Guided Propagation

AbstractObjectiveMycobacterium tuberculosis (Mtb) is an airborne, contagious bacterial pathogen that causes widespread infections in humans. Using Mycobacterium marinum (Mm), a surrogate model organism for Mtb research, the present study develops a deep learning-based scheme that can classify the Mm-infected and uninfected macrophages in tissue culture solely based on morphological changes.MethodsA novel weak-and semi-supervised learning method is developed to detect and extract the cells, firstly. Then, transfer learning and fine-tuning from the CNN is built to classify the infected and uninfected cells.ResultsThe performance is evaluated by accuracy (ACC), sensitivity (SENS) and specificity (SPEC) with 10-fold cross-validation. It demonstrates that the scheme can classify the infected cells accurately and efficiently at the early infection stage. At 2 hour post infection (hpi), we achieve the ACC of 0.923 ± 0.005, SENS of 0.938 ± 0.020, and SPEC of 0.905 ± 0.019, indicating that the scheme has detected significant morphological differences between the infected and uninfected macrophages, although these differences are hardly visible to naked eyes. Interestingly, the ACC at 12 and 24 hpi are 0.749 ± 0.010 and 0.824 ± 0.009, respectively, suggesting that the infection-induced morphological changes are dynamic throughout the infection. Finally, deconvolution with guided propagation maps the key morphological features contributing to the classification.SignificanceThis proof-of-concept study provides a novel venue to investigate bacterial pathogenesis in a macroscopic level and has a great promise in diagnosis of bacterial infections.

Deep Learning in the Study of Protein-Related Interactions

2020 ◽  
Vol 27 (5) ◽  
pp. 359-369 ◽  
Author(s):  
Cheng Shi ◽  
Jiaxing Chen ◽  
Xinyue Kang ◽  
Guiling Zhao ◽  
Xingzhen Lao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Protein Interactions ◽  
Physiological Data ◽  
Great Promise ◽  
Complex Data ◽  
Protein Protein Interactions ◽  
Learning Patterns ◽  
Introductory Overview ◽  
Protein Research ◽  
Neural Network Theory

: Protein-related interaction prediction is critical to understanding life processes, biological functions, and mechanisms of drug action. Experimental methods used to determine proteinrelated interactions have always been costly and inefficient. In recent years, advances in biological and medical technology have provided us with explosive biological and physiological data, and deep learning-based algorithms have shown great promise in extracting features and learning patterns from complex data. At present, deep learning in protein research has emerged. In this review, we provide an introductory overview of the deep neural network theory and its unique properties. Mainly focused on the application of this technology in protein-related interactions prediction over the past five years, including protein-protein interactions prediction, protein-RNA\DNA, Protein– drug interactions prediction, and others. Finally, we discuss some of the challenges that deep learning currently faces.

scholarly journals Deep Learning-Based Differentiation between Mucinous Cystic Neoplasm and Serous Cystic Neoplasm in the Pancreas Using Endoscopic Ultrasonography

Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1052
Author(s):  
Leang Sim Nguon ◽  
Kangwon Seo ◽  
Jung-Hyun Lim ◽  
Tae-Jun Song ◽  
Sung-Hyun Cho ◽  
...  
Keyword(s):  
Decision Making ◽  
Deep Learning ◽  
Network Model ◽  
Endoscopic Ultrasonography ◽  
Data Augmentation ◽  
Clinical Information ◽  
Training Data ◽  
Fine Tuning ◽  
Cystic Neoplasm ◽  
Cystic Neoplasms

Mucinous cystic neoplasms (MCN) and serous cystic neoplasms (SCN) account for a large portion of solitary pancreatic cystic neoplasms (PCN). In this study we implemented a convolutional neural network (CNN) model using ResNet50 to differentiate between MCN and SCN. The training data were collected retrospectively from 59 MCN and 49 SCN patients from two different hospitals. Data augmentation was used to enhance the size and quality of training datasets. Fine-tuning training approaches were utilized by adopting the pre-trained model from transfer learning while training selected layers. Testing of the network was conducted by varying the endoscopic ultrasonography (EUS) image sizes and positions to evaluate the network performance for differentiation. The proposed network model achieved up to 82.75% accuracy and a 0.88 (95% CI: 0.817–0.930) area under curve (AUC) score. The performance of the implemented deep learning networks in decision-making using only EUS images is comparable to that of traditional manual decision-making using EUS images along with supporting clinical information. Gradient-weighted class activation mapping (Grad-CAM) confirmed that the network model learned the features from the cyst region accurately. This study proves the feasibility of diagnosing MCN and SCN using a deep learning network model. Further improvement using more datasets is needed.

scholarly journals Multimodal Deep Learning and Visible-Light and Hyperspectral Imaging for Fruit Maturity Estimation

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1288
Author(s):  
Cinmayii A. Garillos-Manliguez ◽  
John Y. Chiang
Keyword(s):  
Deep Learning ◽  
Visible Light ◽  
Hyperspectral Imaging ◽  
Morphological Changes ◽  
Consumer Preference ◽  
Hyperspectral Data ◽  
Sensitivity Analyses ◽  
Deep Convolutional Neural Networks ◽  
Fruit Maturity ◽  
Learning Architectures

Fruit maturity is a critical factor in the supply chain, consumer preference, and agriculture industry. Most classification methods on fruit maturity identify only two classes: ripe and unripe, but this paper estimates six maturity stages of papaya fruit. Deep learning architectures have gained respect and brought breakthroughs in unimodal processing. This paper suggests a novel non-destructive and multimodal classification using deep convolutional neural networks that estimate fruit maturity by feature concatenation of data acquired from two imaging modes: visible-light and hyperspectral imaging systems. Morphological changes in the sample fruits can be easily measured with RGB images, while spectral signatures that provide high sensitivity and high correlation with the internal properties of fruits can be extracted from hyperspectral images with wavelength range in between 400 nm and 900 nm—factors that must be considered when building a model. This study further modified the architectures: AlexNet, VGG16, VGG19, ResNet50, ResNeXt50, MobileNet, and MobileNetV2 to utilize multimodal data cubes composed of RGB and hyperspectral data for sensitivity analyses. These multimodal variants can achieve up to 0.90 F1 scores and 1.45% top-2 error rate for the classification of six stages. Overall, taking advantage of multimodal input coupled with powerful deep convolutional neural network models can classify fruit maturity even at refined levels of six stages. This indicates that multimodal deep learning architectures and multimodal imaging have great potential for real-time in-field fruit maturity estimation that can help estimate optimal harvest time and other in-field industrial applications.

scholarly journals Unsupervised Multi-Level Feature Extraction for Improvement of Hyperspectral Classification

2021 ◽  
Vol 13 (8) ◽  
pp. 1602
Author(s):  
Qiaoqiao Sun ◽  
Xuefeng Liu ◽  
Salah Bourennane
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Spatial Information ◽  
Hyperspectral Data ◽  
Great Promise ◽  
Learning Models ◽  
Single Level ◽  
Multiple Networks ◽  
Multi Level ◽  
Hyperspectral Classification

Deep learning models have strong abilities in learning features and they have been successfully applied in hyperspectral images (HSIs). However, the training of most deep learning models requires labeled samples and the collection of labeled samples are labor-consuming in HSI. In addition, single-level features from a single layer are usually considered, which may result in the loss of some important information. Using multiple networks to obtain multi-level features is a solution, but at the cost of longer training time and computational complexity. To solve these problems, a novel unsupervised multi-level feature extraction framework that is based on a three dimensional convolutional autoencoder (3D-CAE) is proposed in this paper. The designed 3D-CAE is stacked by fully 3D convolutional layers and 3D deconvolutional layers, which allows for the spectral-spatial information of targets to be mined simultaneously. Besides, the 3D-CAE can be trained in an unsupervised way without involving labeled samples. Moreover, the multi-level features are directly obtained from the encoded layers with different scales and resolutions, which is more efficient than using multiple networks to get them. The effectiveness of the proposed multi-level features is verified on two hyperspectral data sets. The results demonstrate that the proposed method has great promise in unsupervised feature learning and can help us to further improve the hyperspectral classification when compared with single-level features.

scholarly journals Interpretable deep learning for the remote characterisation of ambulation in multiple sclerosis using smartphones

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Andrew P. Creagh ◽  
Florian Lipsmeier ◽  
Michael Lindemann ◽  
Maarten De Vos
Keyword(s):  
Multiple Sclerosis ◽  
Deep Learning ◽  
Inertial Sensor ◽  
Heterogeneous Data ◽  
Fine Tuning ◽  
Sensor Data ◽  
Support Vector ◽  
Deep Convolutional Neural Networks ◽  
Healthcare Applications ◽  
Feature Based

AbstractThe emergence of digital technologies such as smartphones in healthcare applications have demonstrated the possibility of developing rich, continuous, and objective measures of multiple sclerosis (MS) disability that can be administered remotely and out-of-clinic. Deep Convolutional Neural Networks (DCNN) may capture a richer representation of healthy and MS-related ambulatory characteristics from the raw smartphone-based inertial sensor data than standard feature-based methodologies. To overcome the typical limitations associated with remotely generated health data, such as low subject numbers, sparsity, and heterogeneous data, a transfer learning (TL) model from similar large open-source datasets was proposed. Our TL framework leveraged the ambulatory information learned on human activity recognition (HAR) tasks collected from wearable smartphone sensor data. It was demonstrated that fine-tuning TL DCNN HAR models towards MS disease recognition tasks outperformed previous Support Vector Machine (SVM) feature-based methods, as well as DCNN models trained end-to-end, by upwards of 8–15%. A lack of transparency of “black-box” deep networks remains one of the largest stumbling blocks to the wider acceptance of deep learning for clinical applications. Ensuing work therefore aimed to visualise DCNN decisions attributed by relevance heatmaps using Layer-Wise Relevance Propagation (LRP). Through the LRP framework, the patterns captured from smartphone-based inertial sensor data that were reflective of those who are healthy versus people with MS (PwMS) could begin to be established and understood. Interpretations suggested that cadence-based measures, gait speed, and ambulation-related signal perturbations were distinct characteristics that distinguished MS disability from healthy participants. Robust and interpretable outcomes, generated from high-frequency out-of-clinic assessments, could greatly augment the current in-clinic assessment picture for PwMS, to inform better disease management techniques, and enable the development of better therapeutic interventions.

scholarly journals Phage Resistance Is Associated with Decreased Virulence in KPC-Producing Klebsiella pneumoniae of the Clonal Group 258 Clade II Lineage

2021 ◽  
Vol 9 (4) ◽  
pp. 762
Author(s):  
Lucia Henrici De Angelis ◽  
Noemi Poerio ◽  
Vincenzo Di Pilato ◽  
Federica De Santis ◽  
Alberto Antonelli ◽  
...  
Keyword(s):  
Klebsiella Pneumoniae ◽  
Parental Strain ◽  
Bacterial Infections ◽  
Capsular Polysaccharide ◽  
Galleria Mellonella ◽  
Bacterial Pathogen ◽  
Phage Resistance ◽  
Infection Model ◽  
Lytic Phage ◽  
Susceptible Host

Phage therapy is now reconsidered with interest in the treatment of bacterial infections. A major piece of information for this application is the definition of the molecular targets exploited by phages to infect bacteria. Here, the genetic basis of resistance to the lytic phage φBO1E by its susceptible host Klebsiella pneumoniae KKBO-1 has been investigated. KKBO-1 phage-resistant mutants were obtained by infection at high multiplicity. One mutant, designated BO-FR-1, was selected for subsequent experiments, including virulence assessment in a Galleria mellonella infection model and characterization by whole-genome sequencing. Infection with BO-FR-1 was associated with a significantly lower mortality when compared to that of the parental strain. The BO-FR-1 genome differed from KKBO-1 by a single nonsense mutation into the wbaP gene, which encodes a glycosyltransferase involved in the first step of the biosynthesis of the capsular polysaccharide (CPS). Phage susceptibility was restored when BO-FR-1 was complemented with the constitutive wbaP gene. Our results demonstrated that φBO1E infects KKBO-1 targeting the bacterial CPS. Interestingly, BO-FR-1 was less virulent than the parental strain, suggesting that in the context of the interplay among phage, bacterial pathogen and host, the emergence of phage resistance may be beneficial for the host.

Deep Learning Representation from Electroencephalography of Early-Stage Creutzfeldt-Jakob Disease and Features for Differentiation from Rapidly Progressive Dementia

2016 ◽  
Vol 27 (02) ◽  
pp. 1650039 ◽  
Author(s):  
Francesco Carlo Morabito ◽  
Maurizio Campolo ◽  
Nadia Mammone ◽  
Mario Versaci ◽  
Silvana Franceschetti ◽  
...  
Keyword(s):  
Deep Learning ◽  
Supervised Learning ◽  
Early Stage ◽  
Processing System ◽  
Fine Tuning ◽  
Permutation Entropy ◽  
Support Vector ◽  
Progressive Dementia ◽  
Time Frequency ◽  
Jakob Disease

A novel technique of quantitative EEG for differentiating patients with early-stage Creutzfeldt–Jakob disease (CJD) from other forms of rapidly progressive dementia (RPD) is proposed. The discrimination is based on the extraction of suitable features from the time-frequency representation of the EEG signals through continuous wavelet transform (CWT). An average measure of complexity of the EEG signal obtained by permutation entropy (PE) is also included. The dimensionality of the feature space is reduced through a multilayer processing system based on the recently emerged deep learning (DL) concept. The DL processor includes a stacked auto-encoder, trained by unsupervised learning techniques, and a classifier whose parameters are determined in a supervised way by associating the known category labels to the reduced vector of high-level features generated by the previous processing blocks. The supervised learning step is carried out by using either support vector machines (SVM) or multilayer neural networks (MLP-NN). A subset of EEG from patients suffering from Alzheimer’s Disease (AD) and healthy controls (HC) is considered for differentiating CJD patients. When fine-tuning the parameters of the global processing system by a supervised learning procedure, the proposed system is able to achieve an average accuracy of 89%, an average sensitivity of 92%, and an average specificity of 89% in differentiating CJD from RPD. Similar results are obtained for CJD versus AD and CJD versus HC.

Development and Molecular Genetics of Marchantia polymorpha

2021 ◽  
Vol 72 (1) ◽  
Author(s):  
Takayuki Kohchi ◽  
Katsuyuki T. Yamato ◽  
Kimitsune Ishizaki ◽  
Shohei Yamaoka ◽  
Ryuichi Nishihama
Keyword(s):  
Model Organism ◽  
Land Plant ◽  
Marchantia Polymorpha ◽  
Annual Review ◽  
Publication Date ◽  
Molecular Tools ◽  
Genetic Redundancy ◽  
Morphological Differences ◽  
Evo Devo ◽  
Basal Position

Bryophytes occupy a basal position in the monophyletic evolution of land plants and have a life cycle in which the gametophyte generation dominates over the sporophyte generation, offering a significant advantage in conducting genetics. Owing to its low genetic redundancy and the availability of an array of versatile molecular tools, including efficient genome editing, the liverwort Marchantia polymorpha has become a model organism of choice that provides clues to the mechanisms underlying eco-evo-devo biology in plants. Recent analyses of developmental mutants have revealed that key genes in developmental processes are functionally well conserved in plants, despite their morphological differences, and that lineage-specific evolution occurred by neo/subfunctionalization of common ancestral genes. We suggest that M. polymorpha is an excellent platform to uncover the conserved and diversified mechanisms underlying land plant development. Expected final online publication date for the Annual Review of Plant Biology, Volume 72 is May 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

scholarly journals Assessment of Somaclonal Variation in Chrysan-themum (Dendranthema grandiflora Kitam.) using RAPD and Morphological Analysis

1970 ◽  
Vol 18 (2) ◽  
pp. 139-149 ◽  
Author(s):  
P. Kengkarj ◽  
P. Smitamana ◽  
Y. Fujime
Keyword(s):  
Somaclonal Variation ◽  
Morphological Changes ◽  
Field Trials ◽  
Plant Production ◽  
Dendranthema Grandiflora ◽  
Shape Deviation ◽  
Floral Color ◽  
Rapd Pattern ◽  
Morphological Differences ◽  
Highly Correlated

Novel chrysanthemum (Dendranthema grandiflora Kitam.) somaclones from seven commercial cultivars were obtained through the petal segments culture. Morphological variation of the derived clones observed from the field trials was found to be cultivar specific. The major variants within the same cultivar were found only color and inflorescence shape deviation, whereby leaf and stem characters remained unchanged. Distinct variations were found in the 'Pinkgin' cultivar that color changed from magenta to red. The morphological differences of the tested somaclones showed high correlation with the RAPD patterns analysis.  The morphological differences of the tested somaclones were shown to be highly correlated using RAPD pattern analysis. RAPD markers, using ten primers could better separate each cultivar at 80% similarity value. All the somaclones could be singly separated at 90% similarity. However, the higher level of variability of RAPD patterns in chrysanthemum rendered these RAPD fragments as good candidates for somaclonal and cultivar identification. The results from this study revealed the potential increase in range of floral color and morphological changes of petal segment culture, thus this technique would be effectively used for novel plant production. Key words:  Petal culture, Dendranthema grandiflora, Somaclonal variation, RAPD, Identification D.O.I. 10.3329/ptcb.v18i2.3396 Plant Tissue Cult. & Biotech. 18(2): 139-149, 2008 (December)

scholarly journals Aerial Imagery Paddy Seedlings Inspection Using Deep Learning

2022 ◽  
Vol 14 (2) ◽  
pp. 274
Author(s):  
Mohamed Marzhar Anuar ◽  
Alfian Abdul Halin ◽  
Thinagaran Perumal ◽  
Bahareh Kalantar
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Production Costs ◽  
Fine Tuning ◽  
Aerial Imagery ◽  
Planting Density ◽  
Deep Convolutional Neural Networks ◽  
Security Issues ◽  
Agriculture Industry ◽  
Paddy Cultivation

In recent years complex food security issues caused by climatic changes, limitations in human labour, and increasing production costs require a strategic approach in addressing problems. The emergence of artificial intelligence due to the capability of recent advances in computing architectures could become a new alternative to existing solutions. Deep learning algorithms in computer vision for image classification and object detection can facilitate the agriculture industry, especially in paddy cultivation, to alleviate human efforts in laborious, burdensome, and repetitive tasks. Optimal planting density is a crucial factor for paddy cultivation as it will influence the quality and quantity of production. There have been several studies involving planting density using computer vision and remote sensing approaches. While most of the studies have shown promising results, they have disadvantages and show room for improvement. One of the disadvantages is that the studies aim to detect and count all the paddy seedlings to determine planting density. The defective paddy seedlings’ locations are not pointed out to help farmers during the sowing process. In this work we aimed to explore several deep convolutional neural networks (DCNN) models to determine which one performs the best for defective paddy seedling detection using aerial imagery. Thus, we evaluated the accuracy, robustness, and inference latency of one- and two-stage pretrained object detectors combined with state-of-the-art feature extractors such as EfficientNet, ResNet50, and MobilenetV2 as a backbone. We also investigated the effect of transfer learning with fine-tuning on the performance of the aforementioned pretrained models. Experimental results showed that our proposed methods were capable of detecting the defective paddy rice seedlings with the highest precision and an F1-Score of 0.83 and 0.77, respectively, using a one-stage pretrained object detector called EfficientDet-D1 EficientNet.

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